English(EN) Probabilistic Machine Learning for Short-Term Atmospheric Seeing Forecasting

大气视宁度预报的机器学习模型基准测试

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员评估了用于短期大气视宁度预报的机器学习模型，旨在改善天文观测和光学通信。该研究将高斯过程（GPs）和一种名为FloTS的基于归一化流的模型与确定性循环神经网络（RNN）和长短期记忆（LSTM）模型进行了比较。结果表明，深度学习模型，特别是LSTM和FloTS，在预测长达两小时的大气视宁度方面取得了更高的准确性。FloTS在概率预报和不确定性量化方面也展示了强大的能力，提供了比GPs更具信息量的预测。 AI

影响推进了大气视宁度预测中的概率预报和不确定性量化，造福了天文学和光学通信。

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arXiv cs.LG TIER_1 English(EN) · Mary-Joe Medlej, Rahul Srinivasan, Simon Prunet, Aziz Ziad, Christophe Giordano · 2026-10-22 04:00

Probabilistic Machine Learning for Short-Term Atmospheric Seeing Forecasting

arXiv:2610.29654v1 Announce Type: new Abstract: Accurate short-term forecasting of atmospheric seeing is of significant interest for astronomical observations and free-space optical communication systems, where rapid variations in optical turbulence can affect system performance …

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Probabilistic Machine Learning for Short-Term Atmospheric Seeing Forecasting

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